Development of a decision-support system to select nature-based solutions for domestic wastewater treatment
Bibliographic record
Abstract
ABSTRACT Nature-based solutions are increasingly used in domestic wastewater treatment, because of their potential to remove contaminants and pathogens from water (e.g., stormwater, river water, wastewater) as well as their provided co-benefits, such as mitigation of the heat island effect or enhanced biodiversity. The transition from traditional grey technologies towards nature-based solutions in domestic wastewater treatment might yield multiple benefits for local communities while enhancing biodiversity. Although some nature-based solutions such as treatment wetlands have been used for decades in domestic wastewater treatment, this is not the case for others such as green walls or roofs, which lack implementation guidelines and design criteria. Aiming to support implementation of nature-based solutions in domestic wastewater treatment, we have developed an online decision-support system for the pre-selection of the best nature-based solution to use in each socio-environmental context and adapted to the needs, as well as an estimate of the required area. Our decision-support system's recommendations are based on an expert knowledge-driven approach, building on two complementary expert knowledge elicitation workshops. We hope the developed online decision-support system will support the transition towards integrating nature-based solutions into urban water and wastewater treatment systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".